Satellite signal based positioning technologies, which are mainly used outdoors, are usually not suited to deliver a satisfactory performance when used for indoor positioning, since satellite signals of global navigation satellite systems (GNSS), like the global positioning system (GPS), do not penetrate through walls and roofs strongly enough for an adequate signal reception indoors. Thus, these positioning technologies are not able to deliver a performance indoors that would enable seamless, equal and accurate navigation experience outdoors and indoors.
Therefore, several dedicated solutions for indoor positioning have been developed and commercially deployed during the past years. Examples comprise solutions that are based on pseudolites, which are ground based GPS-like short-range beacons, ultra-sound positioning solutions, Bluetooth based positioning solutions, cellular network based positioning solutions and wireless local area network (WLAN) based positioning solutions. Such signals, which have originally not been deployed for positioning purposes or which mainly serve another purpose (such as communication and data transmission), may also be referred to as signals of opportunity or opportunity signals.
As an example, a positioning solution based on WLAN (as an example of a communication network) may be divided in two stages, a training stage and a positioning stage.
In the training stage, learning data is collected. The data may be collected in the form of fingerprints that are based on measurements by mobile devices. A fingerprint may contain a location estimate and measurements taken from the radio interface. The location estimate may be for example GNSS based, sensor-based, or manually inputted. Measurements taken from the radio interface may comprise, by way of example, measured radio signal strengths (RSS) and an identification of WLAN access points transmitting the radio signals. The training may be a continuous background process, in which mobile devices of a large number of consumers are continuously reporting measured data to a server. Consumers may consent to a participation in such a data collection, if their device is equipped with the needed functionality. This approach is also referred to as crowd-sourcing. Alternatively or in addition, mobile devices may be used for collecting fingerprints in a systematic manner. Collected fingerprint data may be uploaded to a database in a server or in the cloud, where algorithms may be run to generate radio models of WLAN access points and/or radio maps for positioning purposes.
In the positioning stage, a mobile device may estimate its current location based on own measurements taken from the radio interface and on the data or a subset of data that is available from the training stage. Model data or radio map data that has been generated in the training stage may be transferred to mobile devices by a server via the Internet as assistance data for use in position determinations. Alternatively, model data and/or radio map data may be stored in a positioning server to which the mobile devices may connect to via the Internet for obtaining a position estimate.
A similar approach could be used for a positioning that is based on other types of terrestrial transmitters or on a combination of different types of terrestrial transmitters.
However, these indoor solutions require either deployment of totally new infrastructure (beacons, tags and so on) or manual exhaustive radio-surveying of the buildings including all the floors, spaces and rooms. This is rather expensive and will take a considerable amount of time to build the coverage to the commercially expected level, which in some cases narrowed the potential market segment only to very thin customer base e.g. for health care or dedicated enterprise solutions. Also, the diversity of these technologies makes it difficult to build a globally scalable indoor positioning solution, and the integration and testing will become complex if a large number of technologies is needed to be supported in the consumer mobile devices, such as smartphones.
Thus for an indoor positioning solution to be commercially successful, firstly it should be globally scalable, secondly it should have low maintenance and deployment costs, and thirdly it should offer an acceptable end-user experience. An existing infrastructure in the buildings and/or existing capabilities in the consumer devices should thus be taken into account. Further, existing infrastructures and device capabilities should be used in such a way that makes it possible to not only achieve a precise (e.g. 2-3 m) horizontal positioning accuracy, but also a precise (e.g. close to a 100%) floor detection accuracy.
Keeping the data, which is used for positioning and stored on a positioning server or the mobile device, up to date is another key factor, as the positioning accuracy may be strongly dependent on the up-to-dateness of the data. However, this is impeded by the fact that the infrastructure may change and affect the signals measured by the mobile device. Typical changes of the infrastructure are for example the movement of the corresponding signal source, the development of new constructions or the change of a structure within a building. Thus, using the described approach generally requires the collection of new information from time to time to maintain a good positioning performance. However, it is then a challenge to detect such changes in the infrastructure and to handle the new data with respect to the previous data in an efficient way in order to maintain the required accuracy for positioning. It has turned out to be particularly problematic to detect changes is the infrastructure which are not the result of a change of the location of the signal source but rather the result of other environmental changes. However, if these changes are not detected and considered, the positioning performance will degrade and in the worst case a positioning result will not be usable at all.